Econometric Genetic Programming Outperforms Traditional Econometric Algorithms for Regression Tasks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Novaes:2017:GECCO,
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author = "Andre Luiz Farias Novaes and Ricardo Tanscheit and
Douglas Mota Dias",
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title = "Econometric Genetic Programming Outperforms
Traditional Econometric Algorithms for Regression
Tasks",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "1427--1430",
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size = "4 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3082506",
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DOI = "doi:10.1145/3067695.3082506",
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acmid = "3082506",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, feature
selection, model selection, multiple regression",
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month = "15-19 " # jul,
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abstract = "Econometric Genetic Programming (EGP) evolves multiple
linear regressions through Genetic Programming (GP),
which is responsible for model selection, aiming to
generate high accuracy regressions with potential
interpretability of parameters. It uses statistical
significance as a feature selection tool, directly and
efficiently identifying introns and controlling bloat.
In this paper, EGP is tested against traditional
feature-selection econometric algorithms in regression
tasks - namely Partial Least Squares Regression, Ridge
Regression and Stepwise Forward Regression -
outperforming them in all three datasets. The way EGP
explores search space of possible regressors and models
is crucial for its results. EGP is carefully
constructed considering econometric theory on
cross-sectional datasets, giving rigorous treatment on
topics like homoscedasticity and heteroscedasticity,
statistical inference for estimated parameters and
sampling criteria. It also benefits by the mathematical
proof on accuracy and statistical significance:
accuracy will only increase if the regressor presents a
test's statistics module in a two-sided hypothesis
testing higher than a predefined value.",
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notes = "Also known as \cite{Novaes:2017:EGP:3067695.3082506}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Andre Luiz Farias Novaes
Ricardo Tanscheit
Douglas Mota Dias
Citations